Computer-implemented methods and systems for provision of a correction algorithm for an x-ray image and for correction of an x-ray image, x-ray facility, computer program, and electronically readable data medium

ABSTRACT

A computer-implemented method for provision of a correction algorithm for an x-ray image that was recorded with an x-ray source emitting an x-ray radiation field, a filter facility spatially modulating an x-ray radiation dose, and an x-ray detector is provided. The correction algorithm includes a trained first processing function that, from first input data that includes at least one first physical parameter describing the x-ray radiation field and/or the measurement and at least one second physical parameter describing the spatial modulation of the filter facility, determines first output data. The first output data includes a mask for brightness compensation with regard to the spatial modulation of the filter facility in the x-ray image. The method includes providing first training data, providing an autoencoder for masks, and training of the autoencoder using the first training data. The method also includes determining an assignment rule, and providing the trained first processing function.

This application claims the benefit of DE 10 2021 206 417.5, filed onJun. 22, 2021, which is hereby incorporated by reference in itsentirety.

BACKGROUND

The present embodiments relate to provision of a correction algorithmfor an x-ray image that was recorded with an x-ray source emitting anx-ray radiation field, a filter facility spatially modulating the x-rayradiation dose in the x-ray radiation, and an x-ray detector.

In medical interventions (e.g., minimally invasive interventions), it isknown that the interventions may be carried out under imaging control,in order, for example, to be able to observe the position of a medicalinstrument (e.g., a catheter) relative to the anatomy and/or also to beable to see changes in the anatomy. For various reasons, x-ray imagingis frequently employed in the prior art in such cases, so that, forexample, fluoroscopy images may be recorded using an x-ray facility witha C-arm that is able to be brought into different positions relative tothe patient. Despite this, with such an x-ray facility, there remainssufficient space for medical personnel who are carrying out and/ormonitoring the intervention.

A disadvantage of x-ray imaging, however, is the radiation load thatoccurs. Thus, during x-ray-guided medical procedures, both the patientand also the medical personnel involved are subjected over time to acertain, in some cases not inconsiderable, dose of x-ray radiation. Inorder to reduce this x-ray dose and thus also the risk of healthconsequences possibly correlated therewith, the dose for the patient isto be optimized and the dose for the medical personnel is to beminimized. This is frequently expressed as the ALARA principle, whereinthe acronym stands for “As Low As Reasonably Achievable”. This providesthat the x-ray dose should be as low as possible while maintaining thenecessary image quality.

A variant known in the prior art for reducing the x-ray load providesfor the use of a filter facility spatially modulating the x-rayradiation dose, especially an ROI filter, in order to keep the imagequality for the highly relevant region (e.g., the Region of Interest(ROI)) high, but still be able to provide the surrounding anatomycontext at a lower image quality. An ROI filter is a semi-transparentfilter facility that, for a region not essentially attenuating the x-rayradiation (e.g., a passage opening in the x-ray radiation field) allowsa standard x-ray dose to be provided, while outside this ROI, a filtermaterial provides that a markedly lower x-ray dose is present. The outerregion of the ROI filter surrounding the ROI may, for example, consistof a thin layer of a material strongly absorbing x-ray radiation, suchas tungsten or lead. For example, filter faculties have been proposedthat use a layer of tungsten 0.127 mm thick.

When such a filter facility spatially modulating the x-ray radiationdose is used, a reduction in the brightness also occurs as a consequencewherever the x-ray dose is reduced. In this context, to provide auniform image impression, it is proposed that a brightness correction(e.g., using an additive brightness correction mask) be used in order tostandardize the brightness over the x-ray image to be corrected (e.g.,after application of an algorithmic transformation). However,determining such a mask has proved not to be trivial, since the maskdepends on a plurality of different factors, such as, for example, onsettings of the x-ray source (e.g., of an x-ray tube), x-ray detectorsettings, pre-filtering measures (e.g., the use of copper), location andsize of the ROI, the recorded object, and the jittering of the focuspoint (e.g., due to the electromagnetic field of the tube drive).

An example of an embodiment of an ROI filter is described by US2018/0168524 A1. In the document, a number of stacked exchangeablefilters are fastened in a housing in an x-ray facility. Each filterincludes an ROI opening. In this case, the ROI openings and thus theROIs of at least two of the filters differ. However, other options havealso already been proposed for adapting the ROI of such an ROI filter(e.g., by a system of actuators) that shifts the ROI. A temporal changein the spatial modulation of a filter facility may be based on a userinput, but may also be undertaken (e.g., in real time) by adjusting theview.

With respect to the determination of a brightness correction, maskmodel-based approaches have been proposed, for example, in order toestimate the masks. The model uses parameters such as tube settings,geometry values, and filter settings. Due to the great complexity of theproblem, such model approaches have, however, not proved to be of anygreat value. Therefore, there is also an approach for deriving masks forbrightness adaptation from a calibration measurement (e.g., by usingimages that were recorded without an object (“flat-field images”)). Insuch cases, an x-ray image is recorded with a filter facility, afterwhich a further x-ray image is recorded with same system settings, butwithout the use of the filter facility, however. Then, a brightnesscorrection mask may be derived from the x-ray images by subtracting thex-ray image recorded with the filter facility from the x-ray imagerecorded without the filter facility.

In such calibration measurements, it has been established for an ROIfilter that the values within the ROI in the mask essentially amount tozero, and in the outer region, the values lie at an essentially constanthigher value. A smooth transition between the values within the ROI andoutside the ROI has, however, been established. This creates theimpression that a model-based correction for deriving a brightnesscorrection mask would have to be conceivable; it has also beendetermined, however, that the shape of the ROI in the mask deviates fromthe shape of the ROI at the ROI filter. For example, the mapping of theROI in the mask is not circular, although this is true for the ROI ofthe filter facility. This may have different causes (e.g., the jitteringof the focus point of the x-ray source and/or the location of thephysical ROI filter in relation to the x-ray radiation field).

The derivation of a brightness correction mask has proved to be evenmore difficult in the presence of the object to be imaged (e.g., of apatient). In addition to the factors already mentioned (e.g., ajittering focus point and the arrangement of the physical ROI filter inthe x-ray radiation field), stray radiation, beam hardening, and heeleffects arise here, which likewise play a significant role. Thus, theuse of a model-based approach dependent of various system parametersmostly proves to be inadequate for compensating for the brightness inthe x-ray image (e.g., in the transition area from the ROI to the outerregion).

One possible approach has been proposed in an article by S. Schafer etal., “Filtered region of interest cone-beam rotational angiography,”Medical Physics 37 (2010), pages 694-703. In this approach, the pixelsin the transition area are reconstructed using complicatedinterpolation-based approaches. These approaches, however, often proveto be not adequate enough, too complicated in robust implementation, andunsuitable for use in real time, which, however, is of significance forthe monitoring of medical interventions.

SUMMARY AND DESCRIPTION

The scope of the present invention is defined solely by the appendedclaims and is not affected to any degree by the statements within thissummary.

The present embodiments may obviate one or more of the drawbacks orlimitations in the related art. For example, an improved option forbrightness correction in x-ray images recorded with a filter facilityspatially modulating an x-ray dose in an x-ray radiation field (e.g., anROI filter) that, for example, brings a high image quality and real timecapability is provided.

In a computer-implemented method for providing a correction algorithmfor an x-ray image that was recorded with an x-ray source emitting anx-ray radiation field, a filter facility spatially modulating the x-rayradiation dose in the x-ray radiation field (e.g., an ROI filter), andan x-ray detector, there is provision in accordance with the presentembodiments for the correction algorithm to include a trained firstprocessing function. The trained first processing function, from firstinput data, which includes at least one first physical parameterdescribing the x-ray radiation field and/or the measurement and at leastone second physical parameter describing the spatial modulation of thefilter facility, determines first output data. The first output dataincludes a mask for brightness compensation with regard to the spatialmodulation of the filter facility in the x-ray image. The methodincludes providing first training data including first training datasetseach with a mask. Each first training dataset is assigned the first andsecond physical parameters of the first input data assigned fordetermining the mask. The method includes providing an autoencoder formasks. The autoencoder has an encoder for determining a latent spacerepresentation of the mask and a decoder for determining a comparisonmask from the latent space representation. The method includes trainingof the autoencoder using the first training data. The method includesdetermining an assignment rule between the physical parameters of thefirst input data that are assigned to the first training datasets, andthe latent space representations of the masks of the first trainingdataset in each case. The method includes providing the trained firstprocessing function as a combination of the assignment rule and thetrained decoder.

This first method of the present embodiments may thus be a provisionmethod (or also training method) for providing a correction algorithmthat makes improved brightness correction possible. In this case, thesolution are described below both with respect to provision methods andsystems and also of correction methods and systems, an x-ray facility,as well as corresponding computer programs and/or electronicallyreadable data media. Features, advantages, or forms of embodiment may betransferred in this case between the different subject matter. In otherwords, this provides that methods and systems for the provision may beimproved by features that are described in the context of methods andsystems for correction, and vice versa.

The present embodiments employ artificial intelligence in the form oftrained processing functions. In general, a trained function, which mayalso be referred to as an algorithm of artificial intelligence, mapscognitive functions that are associated with the functioning of thehuman mind. Through training based on training data, the trainedfunction is in a position to adapt to new circumstances and to detectand to extrapolate patterns.

Generally, parameters of a trained function may be adapted by training.For example, supervised training, semi-supervised training, unsupervisedtraining, reinforcement learning, and/or active learning may be used.Within the framework of the present embodiments, for example,representation learning (e.g., feature learning) is employed for thefirst processing function. Generally, the parameters of a trainedfunction may be adapted iteratively by a number of training steps.

A trained function may, for example, include a neural network, a SupportVector Machine (SVM), a decision tree, and/or A Bayesian network, and/ora die trained function may be based on k-means clustering, Q learning,genetic algorithms, and/or assignment rules. For example, a neuralnetwork may be a deep neural network, a convolutional neural network(CNN), or a deep CNN. A neural network may further be an adversarialnetwork, a deep adversarial network, and/or a generative adversarialnetwork (GAN). Within the framework of the present embodiments, trainedprocessing functions, for example, include at least one CNN.

The aim of the provision method is to provide a correction algorithmthat determines, from input data of the correction algorithm, a maskthat makes possible a brightness compensation in an x-ray image to becorrected in real time and with an image quality that is asartifact-free as possible. The mask thus involves a brightnesscorrection mask that may be applied additively.

To do this, the first basic provision is to train a first processingfunction based on training data masks that have been determined fordifferent system settings, described by the first and the secondphysical parameters. In this case, it has been recognized that bothfirst physical parameters, which relate to the system settings of thex-ray facilities in general (e.g., the creation of the x-ray radiation),the recording geometry, and the detector settings), and also secondphysical parameters, which describe the filter facility and its currentstate, should be used. In such cases, the first and second physicalparameters may cover at least the system settings that are seen asessential in their influence on the different brightness in the x-rayimage to be corrected.

The mask images of the first training data are now used to train anautoencoder (e.g., a convolutional autoencoder comprising a CNN). Duringthis process, the encoder of the autoencoder is trained to describe theinput mask by a minimal set of characteristics (e.g., a latent spacerepresentation). The decoder of the autoencoder is trained to use thecharacteristics encoded as latent space representation and toreconstruct the mask input. This provides that a comparison mask isobtained as output of the autoencoder, of which the difference to themask input of the first training data is minimized.

The idea underlying the further procedure is that now, for the mostprecise possible reconstruction of the mask from the latent spacerepresentation (e.g., the minimal set of characteristics), similar masksshould have a similar latent space representation. In other words, masksthat differ due to a change in one of the physical parameters, whichactually represent system settings/recording parameters, have acomparable latent space representation in the latent space. A number ofdifferences in the latent space parameters occur that relate to thecharacteristics that relate to the corresponding first or secondphysical parameters. If, for example, the mapping geometry regarding theROI changes (e.g., this is mapped with changes), differences occur inthe latent space parameters of the latent space representation thatdescribe the location of the ROI in the mask.

If the first and second physical parameters of the first training datanow cover a possible region of interest of first and second physicalparameters that may be set, the relationship between the physicalparameters and the associated latent space representations described byan assignment rule may be generalized starting from the measurementpoints present through the training datasets (e.g., by fitting and/orinterpolation and/or extrapolation). Thus, through the assignment rule,latent space representations may also be determined for sets of firstand second physical parameters that were not contained in the firsttraining data. The trained decoder supplies the corresponding mask forthis.

Thus, the first processing function consists of a combination of theassignment rule and the decoder and, as first input data for differentsets of first and second physical parameters describing system settings,delivers a suitable mask for these system settings.

This method of operation brings with it a plurality of advantages. Anapproach based on machine learning, which is based on x-ray physics, isprovided to reduce artifacts through the use of a filter facility thatmodulates the x-ray radiation dose spatially and thus to improve theimage quality. Through the correction algorithm, a workflow able to becarried out in real time for analysis and correction of artifacts due tothe filter facility is provided. In this case, the proposedphysics-driven approach makes possible representation learning and theinterpolation of the latent space parameters learned, so that thenecessity of recording an unmanageable plurality of calibration imagesat different system settings is also avoided in order to determinesuitable mask images. In this way, the time for an exhaustingcalibration of different x-ray facilities is saved and yet an effectivecorrection is still provided.

In one embodiment, there may be provision for a greater number of latentspace parameters of the latent space representation to be used asphysical parameters of the first input data. In specific terms, forexample, 3 to 30 physical parameters of the first input data and/or 3 to30 latent space parameters of the latent space representation may beused. The use of a greater number of latent space parameters as physicalparameters brings with it the advantage that ultimately anoverdetermination is present, which makes possible the determination ofthe assignment rule in an especially reliable way. For example, whenfour first and second physical parameters are used, ten latent spaceparameters of the latent space representation are used; when ten firstand second physical parameters are used, twenty-five latent spaceparameters are used. Other embodiments may also be provided.

In one embodiment, the determination of the at least one functionalrelationship (e.g., an assignment rule exclusively containing functionalrelationships) may take place at least partly by fitting and/or byinterpolation and/or by extrapolation. This provides that the assignmentrule is kept as simple as possible, so that the assignment rule may beimplemented in an uncomplicated manner (e.g., with respect to the realtime capability in a corresponding correction system). It has been shownin such cases that simple functional relationships are actually entirelysufficient, which may then be parameterized for determining theassignment rule accordingly through techniques of fitting, ofinterpolation, and/or of extrapolation. This provides that, in exemplaryembodiments, artificial intelligence itself is not used for theassignment rule.

A plurality of options exists for the actual choice of the first andsecond physical parameters. In such cases, the first physical parameterin each case may at least include one first physical parameter from asubgroup in each case. The subgroups include a subgroup with firstphysical parameters related to the creation and/or modification of thex-ray radiation, a subgroup with first physical parameters related tothe recording geometry, and a subgroup with first physical recordingparameters related to the measurement (e.g., by the x-ray detector).Optionally, a jittering of the focus point of the x-ray source, which isthen, for example, embodied as the x-ray tube, may also be mapped by thefirst physical parameters. This effect is frequently also designated aswobbling in relation to the filter facility. For example, when a secondprocessing function is also used in the correction algorithm, which willbe discussed in greater detail below, effects related to the jitteringof the focus point may also be taken into account by this.

In a specific development of the present embodiments, there may, forexample, be provision for the first physical parameters to be selectedfrom the group comprising: A tube voltage of the x-ray source; a tubecurrent of the x-ray source; a pre-filter parameter and/or an apertureparameter; a distance of the x-ray source to the x-ray detector; adistance of the x-ray source to the filter facility; a distance of thefilter facility from the x-ray detector; a pulse length of the x-raypulse creating the x-ray radiation field; a number of x-ray pulses sincethe beginning of the recording of a series of x-ray images; at least onefocal parameter describing the geometry of the focus point; a zoom ofthe x-ray detector; an orientation of the x-ray detector; and a framerate of the x-ray detector. Alternatively or additionally, the secondphysical parameters are selected from the group comprising: A materialof the filter facility; at least one filter thickness parameter (e.g.,describing the course of a filter thickness); and at least one timeparameter describing a change over time of the spatial modulation.

Parameters for creating the x-ray radiation or for influencing the x-rayradiation before the x-ray radiation reaches the filter facility (e.g.,a tube voltage, a tube current, a pre-filter parameter, and/or anaperture parameter) thus, for example, describe the strength and alsothe shape of the x-ray radiation field that arises (e.g., the presenceof a fan beam or cone beam geometry as well as the basic x-ray dose orits distribution). All these system settings ultimately give indicationsof what would have been measured at the x-ray detector if no filterfacility were present. This, however, may also be relevant for thefilter effect of the filter facility itself. Also important here is therecording geometry (e.g., also already in relation to the filterfacility as object), since it is a matter of the completion of themapping of the spatially modulating filter facility at the x-raydetector. Variables known and also useful within the framework of thepresent embodiments in this regard include the distance of the x-raysource to the x-ray detector, often also referred to as the source imagedistance (SID), the distance of the x-ray source to the filter facility,which forms the object (e.g., often also referred to as the sourceobject distance (SOD)), and the distance of the filter facility from thex-ray detector (e.g., object image distance (OID)). In this case, it isalready sufficient for two of these parameters to be known, since thethird follows from them.

Effects of the jittering of the focus point may also be important. Thismay arise, for example, due to electromagnetic fields. Since the filterfacility is usually arranged relatively close to the focus point (e.g.,at a distance of 3 to 10 cm, such as 5 cm), such small “jitter effects”of the “jittering” may be magnified to a “wobbling” of specificsubregions of the filter facility (e.g., of an ROI). Since the jitteringmostly follows a specific course of time after the start of the imagerecording, which for the monitoring of a medical intervention, relatesto the recording of a plurality of individual x-ray images (e.g.,frames) after one another, the pulse length of the x-ray pulse creatingthe x-ray radiation field and the number of x-ray pulses since thebeginning of the recording of a series of x-ray images have proven to bea useful first physical parameter (e.g., exactly like a frame rate ofthe x-ray detector). If it is then known, for example, which numberframe since the beginning of the recording of a current series of x-rayimages is present, a deduction may be made about a current position ofthe jittering focus point or the wobbling ROI from a basically knownmovement sequence. In this case, it should be pointed out in general,since the starting point is frequently a pure point-form focus point,that relevant deviations herefrom (e.g., a focal parameter describingthe geometry of the focus point) may likewise be taken into account.Further parameters of the x-ray detector (e.g., the zoom and/or anorientation of the x-ray detector) relate primarily to the existingrecording geometry. However, a movement of the detector (e.g., avibration or jittering there) may also occur and be described by firstphysical parameters.

With regard to the second physical parameters, as well as a secondphysical parameter describing an attenuation effect, reflecting a filtermaterial of the filter facility, these may, for example, reflect thefilter thickness course (e.g., current filter thickness course), forexample, with reference to at least one filter thickness parameter. Inone embodiment, the second physical parameters may also be a timeparameter describing a change over time of the spatial modulation (e.g.,when information about this is available, such as a new position for theROI has just been moved to), which results from a user input and isundertaken with a specific speed profile, or which consists of a clearlypredefined tracking task for a medical instrument (e.g., a catheter).Other effects able to be described by a time parameter may also be a“jittering” of the filter facility, (e.g., during operation of motors).Since with eye tracking it is rather difficult to make a prediction,there may be provision to postpone such effects to a further processingstage still to be discussed further below.

In one embodiment, the first training datasets may be determined fromx-ray images recorded with and without filter facility (e.g., asflat-field x-ray images). Thus, the process that has frequently servedto date as a calibration measurement may be used to create the firsttraining data, in that recordings without patients (e.g., flat-fieldx-ray images) are made. The same system settings (e.g., the same firstand second physical parameters) are used in each case. If (e.g., afterthe usual logarithmic transformation) the x-ray image with the filterfacility is subtracted from the x-ray image without the filter facility,the corresponding mask for the training data is produced. In thiscontext, a correction within the framework of the present embodimentsmay accordingly take place by addition of a mask to be used forcorrection to the x-ray image (e.g., logarithmically transformed image)to be corrected.

In an embodiment, the foundations for a two-stage procedure will beestablished by the correction algorithm, as well as the first processingalgorithm, also including a second processing algorithm. Through this, amask determined by the first processing algorithm from the first inputdata is to be refined with regard to other effects (e.g., as well as thesystem settings), and to be greatly improved again (e.g., also generalconditions). In specific terms, in this type of embodiment, there isprovision for the correction algorithm further to have a secondprocessing function downstream of the first trained processing functionfor refining the mask determined by the first processing function. Thesecond processing function has a generator network that uses as secondinput data an x-ray image for which the refined mask is to bedetermined, and a mask to be refined determined by the trained firstprocessing function for the physical parameters of the second input dataof the second input data. For training the second processing function,second training data including x-ray images of an object (e.g., of apatient) recorded with and without filter facility, with assignedphysical parameters of the first input data, are provided, Adiscriminator network to discriminate between true x-ray images recordedwithout filter facility and corrected x-ray images obtained using therefined mask received as the second output data is provided tosupplement a generative adversarial network (GAN), For training of thegenerator network and the discriminator network, an output of thediscriminator network (e.g., an adversarial loss value) by comparing acorrected x-ray image of a second training dataset with the x-ray imageof the second training dataset recorded without filter facility is usedfor fitting the generator network and the discriminator network. Thecorrection algorithm including a combination of the trained first andthe trained second processing function is provided.

This provides that, in the correction algorithm and thus also in theprovision method, a two-stage process, in which representation learningis combined with a generative adversarial network in order to train afirst processing function and a second processing function, is realized.The first processing function, as its basic assumption, provides a firstestimation of the brightness correction mask (e.g., the mask to berefined) based on the system settings. This is then again greatlyimproved by the second processing algorithm (e.g., in order to takeaccount of effects not able to be derived or unable to be deriveddirectly from the system settings).

In this case, the actual x-ray image to be corrected may also be takeninto consideration. The actual x-ray image to be corrected hasbrightness characteristics that are also attributable to the mappingeffects of the filter facility, which may accordingly be extracted by agenerator network for refining the mask to be refined. In other words,the estimate of the mask due to the current system settings iscalculated by the decoder being applied to the latent spacerepresentation, which corresponds to the system settings. Based on thex-ray images recorded with the filter facility (e.g., after theapplication of a logarithmic transformation), the refinement isundertaken. The generator network in this case may use residual blocks,skip connections, downsampling layers, upsampling layers, and/or spatialtransformation layers. A refined mask is then obtained as output, bywhich a corrected x-ray image (e.g., through addition of the mask to thex-ray image to be corrected of the second input data) may be determined.For training, this corrected x-ray image is compared by thediscriminator network with an x-ray image present in the second trainingdata, recorded without filter facility, since such an image maycorrespond to it. In this case, the x-ray images recorded with andwithout filter facility do not absolutely have to relate to the sameobject or the same conditions, since the discriminator as an adversarialsystem should be able to distinguish the quality as “with” and “without”filter facility effects. In other words, as is usual with a GAN, thegenerator attempts to “deceive” the discriminator and the latterattempts to “see through” the generator. The result of the discriminatoris used in order to adapt the parameters of the generator network and ofthe discriminator network, as is known from GAN concepts, in aniteration step. This provides that “adversarial training” oradversary-based training is used by a discriminator network that is todiscriminate between real images (e.g., in the absence of the filterfacility) and corrected, calculated x-ray images being employed. Theoutput of the discriminator network may be an adversarial loss value(e.g., an “adversarial loss” or “classification loss”). This output maybe used for updating the generator network and the discriminatornetwork. As soon as the training is concluded, the generator networkused as a further processing function will be in a position to deliverrefined masks, after the application of which for correction, correctedx-ray images are obtained that correspond extremely precisely to realx-ray images.

The second processing function may take account of effects that are notable to be derived or are only able to be derived with difficulty fromthe system settings. These effects may, for example, also be the“jittering” of the focus point due to electromagnetic fields due to thedrive frequency of the stator of the x-ray source embodied as the x-raytube. Over and above this, however, as well as such wobbling of regionsgiven by the filter facility such as the ROI, further temporal, forexample, difficult-to-predict effects are taken into account (e.g., arapid change at the filter facility through eye tracking, such as anadjustment for example of an ROI in accordance with current view of aperson conducting a medical intervention (eye tracking)). Also, thesignificant effects of an object to be recorded are taken into account(e.g., including beam hardening and/or scattered radiation effects).Thus, the addition of the second processing function again offers amarked improvement in the quality and robustness of the correctionavailable through the correction algorithm.

This enables extremely realistic, artifact-free corrected x-ray imagesto be created, which allows a significant reduction of the x-ray dose,since even with low doses outstanding effects are obtained.

The present embodiments (e.g., in the embodiment with first and secondprocessing function) also allow a reduction of the costs for thedevelopment and use of specific and expensive techniques for internalscreening of different components within an x-ray tube as x-ray source.This is, for example, because the approach with the second processingfunction may potentially correct all sources of variation of thebrightness, both technically and also specific to the patient, so that,for example, better results are provided for the same or even a lowerdevelopment outlay.

The present embodiments make it possible to record x-ray images in thepresence of a semi-transparent filter facility even with low pulselength, since, either by the first processing function and/or by thesecond processing function, artifacts will be corrected that arisethrough the jittering of the focus point. This, for example, alsopermits the use of dual-energy imaging with the use of asemi-transparent filter facility. Consecutive frames of the same sceneare recorded at short intervals with different energy spectra, whereextremely short pulse lengths are provided in order to guarantee thespatial mapping authenticity.

In one embodiment, there may be provision for the generator network, atleast during its training, to receive as further second input data atleast one boundary condition restricting its second output data. Inspecific terms, the at least one boundary condition may be chosen, forexample, from the group including: A boundary condition restricting thedeviation of the refined mask from the mask to be refined and/or thespace of the possible mask to be refined; a boundary condition providingthe smoothness of the refined mask; and a boundary condition restrictingthe type of the arithmetic operations to obtain the refined mask fromthe mask to be refined.

Boundary conditions may thus, for example, make sure that the refinedmask belongs to the space of the masks that are determined by the firstprocessing function, or at least approximates to the space extremelyclosely. Boundary conditions may further provide smoothness and onlyallow a specific set of arithmetical operations in order to get from themask to be refined to the refined mask. In this way, it is possible,using physically motivated boundary conditions, to prevent thecorrection algorithm from generating unrealistic outputs, which mayirritate medical personnel. Boundary conditions may also contribute toachieving a general brightness stabilization in the corrected x-rayimage. Basically, the use and definition of boundary conditions isalready known in the prior art, so that this does not have to bediscussed in any greater detail here.

The proposed procedure, based on machine learning and driven by x-rayphysics, thus uses boundary conditions that are derived from actualknowledge about the reality and the physics. This provides that thecorrection algorithm delivers masks and thus corrected x-ray images thatare realistic and do not have any confusing artifacts.

As already mentioned, the x-ray images, as is usual in the prior art,before their use (e.g., as training data and/or input data), arelogarithmically transformed. That provides that the x-ray images areprocessed in the state that is later ultimately intended for thedisplay, which may be provided for a brightness compensation.

The correction algorithm provided by the provision method may inpractice be used for correction of x-ray images recorded with a filterfacility. Accordingly, the present embodiments also relate to acomputer-implemented method for correction of an x-ray image that wasrecorded with an x-ray source emitting an x-ray radiation field, afilter facility spatially modulating the x-ray radiation dose (e.g., anROI filter), and an x-ray detector, using a correction algorithmprovided with a method of the present embodiments. The correction methodincludes determining a mask using the correction algorithm from at leastthe first input data assigned to the x-ray image to be corrected, andusing the mask for correction of the x-ray image to be corrected.

In the event of the correction algorithm, as well as the trained firstprocessing function, also including the trained second processingfunction, the first input data and the second input data are jointlyseen as overall input data of the correction algorithm. Initially, thefirst input data (e.g., the first and second physical parameters) isused in order to determine the mask to be refined. The mask to berefined is used jointly with an x-ray image to be corrected of thesecond input data in order to determine the refined mask and use therefined mask for correction of the x-ray image to be corrected. Theremarks relating to the provision method of the present embodiments maybe transferred by analogy to the correction method of the presentembodiment, with which the advantages already stated may likewise beobtained.

In one embodiment, a denoising method may additionally be applied to thecorrected x-ray image. That provides that a further improvement of thecorrected x-ray image with respect to its noise may be made, which alsoallows a reduction of the x-ray dose, since in a denoised correctedx-ray image, structures that previously may have been subject to noiseare easier to recognize. Denoising methods basically known in the priorart may be employed in such cases.

The present embodiments also relate to a system for provision of acorrection algorithm for an x-ray image that was recorded with an x-raysource emitting an x-ray radiation field, a filter facility spatiallymodulating the x-ray radiation dose (e.g., an ROI filter), and an x-raydetector. The correction algorithm includes a trained first processingfunction that, from first input data, which includes at least one firstphysical parameter describing the x-ray radiation field and/or themeasurement and at least one second physical parameter describing thespatial modulation of the filter facility, determines first output datathat includes a mask for brightness compensation relating to the spatialmodulation of the filter facility in the x-ray image. The provisionsystem includes a first training interface for provision of firsttraining data including first training datasets each with a mask. Eachfirst training dataset is assigned the first and second physicalparameters of the first input data assigned for determining the mask.The provision system also includes a first training unit for training ofan autoencoder for masks. The autoencoder has an encoder for determininga latent space representation of the mask and a decoder for determininga comparison mask from the latent space representation, using the firsttraining data. The provision system includes a rule determination unitfor determining an assignment rule between the physical parameters ofthe first input data that are assigned to the first training datasets,and the latent space representations of the masks of the respectivefirst training dataset. The provision system includes a second traininginterface for provision of the trained first processing function as acombination of the assignment rule and the trained decoder.

In other words, the provision system is embodied for carrying out theprovision method of the present embodiments. For this, the provisionsystem may use at least one processor and/or at least one memory device.The corresponding functional units described may be realized by hardwareand/or software.

In one embodiment of the provision system, the correction algorithmfurther has a trained second processing function downstream of the firsttrained processing function for refining the mask determined by thefirst processing function. The second processing function has agenerator network that, as second input data, uses an x-ray image, forwhich the refined mask is to be determined, and a mask to be refineddetermined using the trained first processing function for the physicalparameters of the first input data of the x-ray image of the secondinput data. The provision system may further have a second traininginterface for acceptance of x-ray images of an object (e.g., of apatient) recorded with and without filter facility, with assignedphysical parameters of the first input data. The provision system mayfurther have a second training unit for training of a generativeadversarial network formed from the generator network and adiscriminator network. The discriminator network is for discriminatingbetween true x-ray images recorded without filter facility and correctedx-ray images obtained using the refined mask obtained as second outputdata. The second training unit is embodied for adapting the generatornetwork and the discriminator network using an output of thediscriminator network by comparing a corrected x-ray image of a secondtraining dataset with the x-ray image of the second training datasetrecorded without filter facility. The provision system may further havea fourth training interface for provision of the second correctionalgorithm, which includes a combination of the trained first processingfunction and the trained second processing function.

A system of the present embodiments for correction of an x-ray imagethat was recorded with an x-ray source emitting an x-ray radiationfield, a filter facility spatially modulating the x-ray radiation dose(e.g., an ROI filter), and an x-ray detector has: A first applicationinterface for acceptance of a correction algorithm provided by aprovision system in accordance with the present embodiments; a secondapplication interface for acceptance of the x-ray image to be correctedalong with assigned first and second physical parameters; a maskdetermination unit for determining a mask by the correction algorithmfrom at least the first input data assigned to the x-ray image to becorrected; a correction unit for using the mask for correction of thex-ray image to be corrected; and a third application interface forprovision of the corrected x-ray image.

What has been stated previously also continues to apply in relation tothe correction system, where the correction system is embodied, forexample, for carrying out a correction method of the presentembodiments. The correction system may also have at least one processorand at least one memory device. Once again, hardware and/or software maybe employed for the functional units.

The present embodiments also relate to an x-ray facility having an x-raysource, an x-ray detector, a filter facility spatially modulating thex-ray radiation dose, and a control facility. The control facility has acorrection system in accordance with the present embodiments. Thatprovides that a correction of the x-ray image by brightness compensationmay be undertaken directly at the x-ray facility recording the x-rayimages, which, for example, may be provided during image monitoring ofmedical interventions (e.g., minimally invasive interventions). Forthis, the x-ray facility, for example, may also have a display facility,on which the corrected x-ray image is displayed. The previous statementscontinue to apply for the x-ray facility. Embodiments in which the x-rayfacility, in addition to the correction system, may also have aprovision system in accordance with the present embodiments as part ofthe control facility may also be provided.

The x-ray facility of the present embodiments may be embodied, forexample, as an x-ray facility with a C-arm, on which the x-ray sourceand the x-ray detector are arranged opposite one another. Such C-armx-ray facilities are frequently used in medical interventions forfluoroscopy, so that such C-arm x-ray facilities may also be referred toas interventional C-arm x-ray facilities.

A computer program of the present embodiments is able to be loadeddirectly into a memory device of a computing facility (e.g., a computingfacility of a provision system and/or of a correction system) and/or acontrol facility of an x-ray facility and has program means for carryingout the acts of the correction method and/or provision method describedherein when the computer program is executed on the computing facilityor the control facility. The computer program may be stored on anelectronically readable data medium in accordance with the presentembodiments, which thus includes control information that includes atleast one computer program of the present embodiments and is embodied insuch a way that, when the data medium is used in a computing facility,the medium carries out the acts of a provision method and/or correctionmethod in accordance with the present embodiments. The data medium may,for example, involve a non-volatile data medium (e.g., a non-transitorycomputer-readable storage medium such as a CD-ROM).

BRIEF DESCRIPTION OF THE DRAWINGS

Further advantages and details of the present invention emerge from theexemplary embodiments described below and also with the aid of thedrawings. In the drawings:

FIG. 1 shows an exemplary embodiment of a neural network;

FIG. 2 shows an exemplary embodiment of a convolutional neural network(CNN);

FIG. 3 shows a basic sketch of an embodiment of an x-ray facility;

FIG. 4 shows a recording geometry produced during the recording of apatient;

FIG. 5 shows a filter facility embodied as an ROI filter;

FIG. 6 shows a schematic diagram of an x-ray image to be correctedrecorded using the filter facility in accordance with FIG. 5 ;

FIG. 7 shows a diagram for determining a mask for correction of an x-rayimage to be corrected;

FIG. 8 shows a flow diagram of a first stage of one embodiment of aprovision method;

FIG. 9 shows a flow diagram of a second stage of one embodiment of aprovision method;

FIG. 10 shows a flow diagram of one embodiment of a correction method;

FIG. 11 shows the functional structure of a provision system inaccordance with an embodiment; and

FIG. 12 shows the functional structure of a correction system inaccordance with an embodiment.

DETAILED DESCRIPTION

FIG. 1 shows an exemplary embodiment of an artificial intelligenceneural network 1. Other expressions for such a network are “artificialneural network,” “neural network,” “artificial neural net,” or “neuralnet”.

The artificial neural network 1 includes nodes 6 to 18 and edges 19 to21, where each edge 19 to 21 is a directed connection from a first node6 to 18 to a second node 6 to 18. In general, the first node 6 to 18 andthe second node 6 to 18 are different nodes 6 to 18. In one embodiment,the first node 6 to 18 and the second node 6 to 18 may be identical. Forexample, in FIG. 1 , the edge 19 is a directed connection from the node6 to the node 9, and the edge 21 is a directed connection from the node16 to the node 18. An edge 19 to 21 from a first node 6 to 18 to asecond node 6 to 18 is referred to as the ingoing edge for the secondnode 6 to 18 and as the outgoing edge for the first node 6 to 18.

In this exemplary embodiment, the nodes 6 to 18 of the artificialintelligence neural network 1 may be arranged in layers 2 to 5, wherethe layers 2 to 5 may have an intrinsic order that is introduced by theedges 19 to 21 between the nodes 6 to 18. For example, edges 19 to 21may only be provided between neighboring layers of nodes 6 to 18. In theexemplary embodiment shown, there exists an input layer 110 that onlyhas the nodes 6, 7, 8, without an ingoing edge in each case. The outputlayer 5 includes only the nodes 17, 18, without outgoing edges in eachcase, where further hidden layers 3 and 4 lie between the input layer 2and the output layer 5. In the general case, any number of hidden layers3, 4 may be chosen. The number of the nodes 6, 7, 8 of the input layer 2may correspond to the number of input values in the neural network 1,and the number of nodes 17, 18 in the output layer 5 may correspond tothe number of output values of the neural network 1.

For example, a number (e.g., a real number) may be assigned to the nodes6 to 18 of the neural network 1. In this case, x^((n)) _(i) refers tothe value of the ith node 6 to 18 of the nth layer 2 to 5. The values ofthe nodes 6, 7, 8 of the input layer 2 are equivalent to the input valueof the neural network 1, while the values of the nodes 17, 18 or theoutput layer 5 are equivalent to the output values of the neural network1. Further, edge 19, 20, 21 may be assigned a weight in the form of areal number. For example, the weight is a real number in the interval[−1, 1] or in the interval [0, 1,]. In this case, w^((m,n)) _(i,j)refers to the weight of the edge between the ith node 6 to 18 of the mthlayer 2 to 5 and the jth node 6 to 18 of the nth layer 2 to 5. Theabbreviation w_(i,j) ^((n)) is further defined for the weight w_(i,j)^((n,n+1)).

In order to calculate output values of the neural network 1, the inputvalues are propagated through the neural network 1. For example, thevalues of the nodes 6 to 18 of the (n+1)th layer 2 to 5 may becalculated based on the values of the nodes 6 to 18 of the nth layer 2to 5 byx _(j) ^((n+1)) =f(Σ_(i) x _(i) ^((n)) ·w _(i,j) ^((n))).

In this equation, f is a transfer function that may also be referred toas an activation function. Known transfer functions are step functions,Sigmoid functions (e.g., the logistical function, the generalizedlogistical function, the tangens hyperbolicus, the arcustangens, theerror function, the smooth step function) or rectifier functions. Thetransfer function is essentially used for standardization purposes.

For example, the values are propagated layer-by-layer through the neuralnetwork 1, where values of the input layer 2 are given by the input dataof the neural network 1. Values of the first hidden layer 3 may becalculated based on the values of the input layer 2 of the neuralnetwork 1, values of the second hidden layer 4 may be calculated basedon the values in the first hidden layer 3, etc.

In order to be able to define the values w_(i,j) ^((n)) for the edges 19to 21, the neural network 1 is to be trained using training data. Forexample, training data includes training input data and training outputdata, which are referred to below as t_(i). For a training step, theneural network 1 is applied to the training input data in order todetermine calculated output data. For example, the training output dataand the calculated output data include a number of values, where thenumber is determined as the number of the nodes 17, 18 of the outputlayer 5.

For example, a comparison between the calculated output data and thetraining output data is used in order to recursively fit the weightswithin the neural network 1 (e.g., back propagation algorithm). Forexample, the weights may be changed in accordance withw′ _(i,j) ^((n)) =w _(i,j) ^((n))−γ·δ_(j) ^((n)) ·x _(i) ^((n))where γ is a learning rate, and the numbers δ_(j) ^((n)) may becalculated recursively asδ_(j) ^((n))=(Σ_(k)δ_(k) ^((n+1) ·w _(j,k) ^((n+1)))·f′(Σ_(i) x _(i)^((n)) ·w _(i,j) ^((n)))based on δ_(j) ^((n+1)), when the (n+1)th layer is not the output layer5, andδ_(j) ^((n))=(x _(k) ^((n+1)) −t _(j) ^((n+1)))·f′(Σ_(i) x _(i) ^((n))·w _(i,j) ^((n)))the (n+1)th layer is the output layer 5, where f′ is the firstderivation of the activation function, and yj(n+1) is the comparisontraining value for the jth node 17, 18 of the output layer 5.

Also given below with respect to FIG. 2 is an example for aconvolutional neural network (CNN). In this example, the expression“layer” in the figure is employed in a slightly different way than forclassical neural networks. For a classical neural network, theexpression “layer” only refers to the set of nodes that forms a layer,and thus, to a specific generation of nodes. For a convolutional neuralnetwork, the expression “layer” is often used as an object that activelychanges data (e.g., as a set of nodes of the same generation and eitherthe set of ingoing or outgoing edges).

FIG. 2 shows an exemplary embodiment of a convolutional neural network22. In the exemplary embodiment shown, the convolutional neural network22 includes an input layer 23, a convolutional layer 24, a pooling layer25, a fully connected layer 26, and an output layer 27. In alternateembodiments, the convolutional neural network 22 may contain a number ofconvolutional layers 24, a number of pooling layers 25, and a number offully connected layers 26, just like other types of layers. A givensequence of the layers may be selected, where usually fully connectedlayers 26 form the last layers before the output layer 27.

For example, within a convolutional neural network 22, the nodes 28 to32 of one of the layers 23 to 27 may be arranged in a d-dimensionalmatrix or as a d-dimensional image. For example, in the two-dimensionalcase, the value of a node 28 to 32 may be referred to with the indicesi, j in the nth layer 23 to 27 as x^((n))[i,j]. The arrangement of thenodes 28 to 31 of a layer 23 to 27 does not have any effect as such onthe calculations within the convolutional neural network 22 as such,since these effects are exclusively produced by the structure and theweights of the edges.

A convolutional layer 24 is, for example, characterized in that thestructure and the weights of the ingoing edges form a convolutionoperation based on a specific number of kernels. For example, thestructure and the weights of the ingoing edges may be selected so thatthe values x_(k) ^((n)) of the nodes 29 of the convolutional layer 24are determined as a convolution x_(k) ^((n))=K_(k)*x^((n−1)) based onthe values x^((n−1)) of the node 28 of the preceding layer 23, where theconvolution * in the two-dimensional case may be defined asx _(k) ^((n)) [i,j]=(K _(k) *x ^((n−1)))[i,j]=Σ _(i′)Σ_(j′) K _(k)[i′,j′]·x ^((n−1)) [i−i′,j−j′].

In this equation, the kth kernel K_(k) is a d-dimensional matrix (e.g.,a two-dimensional matrix) that may be small by comparison with thenumber of the nodes 28 to 32 (e.g., a 3×3 matrix or a 5×5 matrix). Forexample, this implies that weights of the ingoing edges are notindependent, but are selected so that the weights create the convolutionequation above. In the example for a kernel that forms a 3×3 matrix,there exist only nine independent weights (e.g., where each entry of thekernel matrix corresponds to an independent weight), regardless of thenumber of the nodes 28 to 32 in the corresponding layers 23 to 27. Forexample, for a convolutional layer 24, the number of the nodes 29 in theconvolutional layer 24 is equivalent to the number of the nodes 28 inthe preceding layer 23 multiplied by the number of the convolutionkernels.

When the nodes 28 of the preceding layer 23 are arranged as ad-dimensional matrix, the use of the plurality of kernels may beunderstood as the insertion of a further dimension, which is alsoreferred to as a depth dimension, so that the nodes 29 of theconvolutional layer 24 are arranged as a (d+1)-dimensional matrix. Whenthe nodes 28 of the preceding layer 23 are already arranged as a(d+1)-dimensional matrix with a depth dimension, the use of a pluralityof convolution kernels may be understood as an expansion along the depthdimension, so that the nodes 29 of the convolutional layer 24 areequally arranged as a (d+1)-dimensional matrix. The size of the(d+1)-dimensional matrix in the depth dimension is greater by the factorformed by the number of the kernels than in the preceding layer 23.

The advantage of using convolution kernels 24 is that the spatiallylocal correlation of the input data may be utilized by a localconnection pattern between nodes of neighboring layers being created(e.g., in that each node only has connections to a small area of thenode of the preceding layer).

In the exemplary embodiment shown, the input layer 23 includesthirty-six nodes 28 that are arranged as a two-dimensional 6×6 matrix.The convolutional layer 24 includes seventy-two nodes 29 that arearranged as two two-dimensional 6×6-matrices, where each of the twomatrices is the result of a convolution of the values of the input layer23 with a convolution kernel. In the same way, the nodes 29 of theconvolutional layer 24 may be understood as being arranged as athree-dimensional 6×6×2 matrix, where the last-mentioned dimension isthe depth dimension.

A pooling layer 25 is characterized in that the structure and theweights of the ingoing edges as well as the activation function of itsnodes 30 define a pooling operation based on a non-linear poolingfunction f. For example, in the two-dimensional case, the values x^((n))of the nodes 30 of the pooling layer 25 may be calculated, based on thevalues x^((n+1)) of the nodes 29 of the preceding layer 24, asx ^((n)) [i,j]=f(x ^((n−1)) [id ₁ ,jd ₂ ], . . . ,x ^((n−1)) [id ₁ +d₁−1,jd ₂ +d ₂−1]).

In other words, the number of nodes 29, 30 may be reduced by the use ofa pooling layer 25, in that a number of d₁×d₂ of neighboring nodes 29 inthe preceding layer 24 is replaced by a single node 30 that iscalculated as a function of the values of the the number of neighboringnodes 29. For example, the pooling function f may be a maximum function,an averaging or the L2 norm. For example, for a pooling layer 25, theweights of the ingoing edges may be defined and not modified bytraining.

The advantage of using a pooling layer 25 is that the number of nodes29, 30 and the number of parameters is reduced. This leads to areduction in the amount of calculations necessary within theconvolutional neural network 22 and thus to a control of theoverfitting.

In the exemplary embodiment shown, the pooling layer 25 involves a maxpooling layer, in which four neighboring nodes are replaced by just onesingle node, the value of which is formed by the maximum of the valuesof the four neighboring nodes. The max pooling is applied to eachd-dimensional matrix of the preceding layer; in this exemplaryembodiment, the max pooling is applied to each of the twotwo-dimensional matrices, so that the number of nodes is reduced fromseventy-two to eighteen.

A fully connected layer 26 is characterized by a plurality (e.g., all)edges being present between the nodes 30 of the preceding layer 25 andthe nodes 31 of the fully connected layer 26, where the weight of eachof the edges may be fitted individually. In this exemplary embodiment,the nodes 30 of the preceding layer 25 and the fully connected layer 26are both shown as two-dimensional matrices and also as non-contiguousnodes (shown as a row of nodes, where the number of the nodes has beenreduced so that the nodes may be shown more easily). In this exemplaryembodiment, the number of nodes 31 in the fully connected layer 26 isequal to the number of the nodes 30 in the preceding layer 25. Inalternate forms of embodiment, the number of the nodes 30, 31 may bedifferent.

Further, in this exemplary embodiment, the values of the nodes 32 of theoutput layer 27 are determined by the softmax function being applied tothe values of the nodes 31 of the preceding layer 26. Throughapplication of the softmax function, the sum of the values of all nodes32 of the output layer 27 is one, and all values of all nodes 32 of theoutput layer are a real number between 0 and 1. When the convolutionalneural network 22 is used for classification of input data, the valuesof the output layer 27, for example, may be interpreted as theprobability of the input data falling into one of the different classes.

A convolutional neural network 22 may likewise have a ReLU layer, whereReLU is an acronym for “rectified linear units”. For example, the numberof the nodes and the structure of the nodes within a ReLU layer isequivalent to the number of the nodes and the structures of the nodes ofthe preceding layer. The value of each node in the ReLU layer may becalculated, for example, by application of a rectifier function to thevalue of the corresponding node of the preceding layer. Examples ofrectifier functions are f(x)=max(0,x), the tangens hyperbolicus, or theSigmoid function.

Convolutional neural networks 22 may be trained, for example, based onthe back propagation algorithm. In order to avoid an overfitting,methods of regularization may be employed (e.g., dropout of individualnodes 28 to 32), stochastic pooling, use of artificial intelligencedata, weight decomposition based on the L1 or the L2 standard, ormaximum standard restrictions.

FIG. 3 shows a basic sketch of one embodiment of an x-ray facility 33.The x-ray facility 33 has a C-arm 34, on which an x-ray source 35 (e.g.,an x-ray tube) and an x-ray detector 36 are arranged opposite oneanother. The x-ray facility 33 further includes a filter facility 37(e.g., an ROI filter) that may be used, for example, for x-raymonitoring of medical interventions (e.g., minimally invasiveinterventions) for reducing the x-ray radiation dose for the patient andthe medical personnel. The operation of the x-ray facility 33 iscontrolled by a control facility 38 that is embodied as a correctionsystem 39 for correction of x-ray images for balancing brightnessrecorded with the filter facility 37, since the filter facility 37 isembodied for spatial modulation of the x-ray radiation dose. Optionally,the control facility 38 may also include a provision system 40. Whilethe correction system 39 is embodied for carrying out a correctionmethod of one or more of the present embodiments, the provision system40 is embodied for carrying out a provision method of one of more of thepresent embodiments, which will be explained in greater detail below.

FIG. 4 shows in greater detail the recording geometry when the filterfacility 37 is used. The x-ray source 35, starting from a focus point41, creates an x-ray radiation field 42 in order to record a recordingregion of a patient 43 only shown schematically here. This may alsoresult in effects lying outside the filter facility 37, which relate tothe mapping of the filter facility 37 on the x-ray detector 36; inaccordance with the present embodiments, these are likewise to be takeninto account in a brightness compensation (e.g., the determination of abrightness correction mask). Thus, with an x-ray tube, for example, thismay result in a “jittering” of the focus point 41, which, due to thefilter facility 37 being arranged close to the focus point 41 (e.g., ata distance of 5 cm) may be mapped in a relevant way as a “wobbling” ofspecific regions. It may further also be relevant that the focus point41 is mostly not completely punctiform, but has a specific geometry.Also, the patient 43, by beam hardening, scattered radiation, and thelike, may have an influence on the mapping of the filter facility 37 atthe x-ray detector 36, where the setting of the x-ray detector 36 itselfis to be further mentioned. For determining a correct brightnesscorrection mask to be used for brightness compensation, not onlyknowledge about the filter facility 37 and its state during imaging isrelevant, but also about other aspects (e.g., system settings fordynamics of the situation and influences of the patient 43).

FIG. 5 shows an exemplary embodiment of the filter facility 37 as an ROIfilter in more detail. The filter facility 37 consists of a filtermaterial 44 (e.g., tungsten) that has a central passage opening 46 thatdefines an ROI 45, in which the standard dose of x-ray radiation isstill present, while outside the ROI 45, a strong attenuation of thex-ray radiation through the filter material 44 (e.g., a thin tungstenlayer) is present. The filter facility 37 may further have an actuatorsystem 47, by which the size and/or position of the ROI 45 may be ableto be changed (e.g., by exchanging various filter plates available,movement of the filter material 44, or the like). The actuator system 47may be actuated, for example, as a function of user inputs, for trackinga medical instrument (e.g., a catheter), and/or as a function of eyetracking data.

FIG. 6 shows a schematic of an x-ray image 48 to be corrected that wasrecorded using the filter facility 37 of FIG. 5 . The ROI 45 is mappedclearly visibly in the x-ray image 48, since there, the anatomy 49 ofthe patient 43 appears far brighter than in the outer regionssurrounding the ROI 45, as is indicated by the cross hatching. As hasalready been explained, a smooth transition is mostly produced in thiscase, where, however, the geometrical form of the opening 46, due todynamic effects, such as the jittering of the focus point 41, is notnecessarily reproduced in the exact correct form.

FIG. 7 shows an option for how a mask may be determined, such as atleast with regard to system settings and their effects (e.g., in acalibration measurement). In this case, two x-ray images 50, 51 withouta patient 43 with specific, fixed system settings, described by firstand second physical parameters, may be recorded. The x-ray image 50 is aflat-field x-ray image without the filter facility 37 as object, whilethe x-ray image 51 is a flat-field x-ray image for which the filterfacility 37 has been used. If the x-ray image 51 is now subtracted fromthe x-ray image 50 (cf., act 52), a mask 53 is produced. In this case,the x-ray images 50, 51 are logarithmically transformed, as usual,before determination of the mask 53. The mask 53 may be employed foradditive correction of an x-ray image 48 to be corrected, when exactlythe same system settings are present, where, however, the describeddynamic effects as well as the effects by the patient 43 may then not betaken into account. This, however, makes possible the procedure inaccordance with the exemplary embodiments described here, with whichextremely expensive calibration measurements for all conceivable systemsettings are avoided.

A provision method in accordance with the present embodiments is nowdescribed with reference to FIGS. 8 and 9 , where FIG. 8 relates to thetraining of the first processing function, thus to the first stage, andFIG. 9 relates to the training of the second stage, thus to the secondprocessing function. By combination of the trained first processingfunction and the trained second processing function, a highly accuratemask for correction of x-ray images 48, encompassing many effects foruse in a correction algorithm of the present embodiments, may beprovided.

The first processing algorithm relates to the characteristics of themeasurement itself (e.g., the technical settings or system settings,such as creation of the x-ray radiation, geometry, detector operation,characteristics, or settings of the filter facility). In order to trainthe first processing algorithm, in acts S1 and S2, using the methoddescribed for FIG. 7 , first training data is recorded, where eachtraining dataset includes a mask 53 and also the first and secondphysical parameters 54, 55 used in the recording of the mask 53, to beused as first input data.

The first and second physical parameters describe system settings of thex-ray facility 33 used. For example, first physical parameters areconcerned with the x-ray radiation field, its creation, and themeasurement, also including the recording geometry and the operation ofthe x-ray detector 36, and second physical parameters 55 are explicitlyconcerned with the characteristics of the filter facility 37. Forexample, the second physical parameters 55 may relate to the filtermaterial, the filter material thickness, as well as the size and thelocation of the ROI 45, if necessary also temporarily by a timeparameter. First physical parameters 54 may, for example, relate tosettings of the x-ray source (e.g., tube current, tube voltage, andpulse length), focus point settings (e.g., focus point sizes andangles), detector settings (e.g., zoom, orientation, and frame rate), aswell as the recording geometry (e.g., SID, SOD and OID, where the filterfacility 37 counts as the object). In this case, the physical parameters54, 55 to be used in the calibration are, where possible, chosen so thatthe physical parameters 54, 55 cover the setting space relevant for theactual recordings.

With the settings of the first and second physical parameters, in actS1, x-ray images 50 and 51 are recorded, as described for FIG. 7 . Inact S2, respective masks 53 are determined from the x-ray images 50 and51, as described. With the assignment of the first and second physicalparameters 54, 55 to these first masks, the first training data isdetermined, so that an autoencoder 56 for the masks 53, as is basicallyknown, may be trained, which is indicated by the arrow 57. Theautoencoder 56 includes, as is basically known, at least one CNN, wherean encoder 58 and a decoder 59 are formed. A latent space representation60 with just a few latent space parameters is determined by the encoder58, which should be sufficient for the mask 53 to be reproduced asexactly as possible by the decoder 59. In other words, the decoder 59,during the training in accordance with arrow 57, delivers comparisonmasks 61 that may be compared with the respective input mask 53 of therespective training data, where the deviation is minimized.

A relationship exists between the latent space representation 60 and thefirst and second physical parameters. Sets of first and second physicalparameters, between which there are only few or small changes, deliversimilar latent space representations 60 in this case. If a physicalparameter 54, 55 changes the recording, latent space parameters alsohaving a relationship with this physical parameter, which indeed maprelevant characteristics, change. If, for example, the distance changesbetween the x-ray source 35 and the x-ray detector 36 (SID), where thedistance from the x-ray source 35 to the filter facility 37 (SOD)remains constant, however, the opening 46 is mapped larger, so thatchanges in latent space parameters relating to this characteristicoccur. The fact that a relationship exists at least with a part of thefirst and second physical parameters 54, 55 is utilized in act S3 inorder to determine an assignment rule 62 from latent spacerepresentations 60 (e.g., in specific terms, latent space parameters) toany given sets of first and second physical parameters 54, 55, which maycontain functional relationships between the first and second physicalparameters 54, 55 and the latent space parameters of the latent spacerepresentation 60. The functional relationships may be parameterized byfitting and/or interpolation and/or extrapolation. Accordingly, theassignment rule 62 for any given system settings (e.g., any given firstand second physical parameters 54, 55) allows the associated latentspace representation 60 to be determined, so that using the traineddecoder 59, a rough estimation of a mask used in training of the secondprocessing function accordance with FIG. 9 may be determined.Accordingly, for the first stage, as output of the trained firstprocessing function 63 (cf., FIG. 9 ), a combination of the assignmentrule 62 and the decoder 59 may be provided. This provides that thetrained first processing function uses as input data the first andsecond physical parameters 54, 55 in order to output as first outputdata a mask 64 still to be refined by the second stage.

In accordance with FIG. 9 , the second processing function is trainedand provided. In order to now first determine the second training data,in act S4 taking place over a longer period of time, pairs of images ofx-ray images 65 of different patients without filter facility 37 andx-ray images of the same patient in each case using the same physicalparameters 54, 55 with filter facility 13 are recorded and in act S5logarithmically transformed, as is basically known. This provides thatthe second training data includes a pair of images consisting of onex-ray image 65 that was recorded without filter facility 13 and an x-rayimage 66 that was recorded with filter facility 37, as well as the firstand second physical parameters 54, 55 used for the recording, associatedfirst input data (shown by way of summary in FIG. 9 as first input datawith the reference character 67). The x-ray images 65, 66 in exemplaryembodiments do not necessarily have to show the same object and/or thesame point in time, but certainly may.

Due to the trained first processing function 63, it is possible, foreach pair of images 65, 66, using this mask 64 to be refined, todetermine a generator network 69 that forms the second processingfunction 70 as first output data. These, together with boundaryconditions 68 still to be discussed as well as the respective x-rayimages 66 to be corrected, recorded with the filter facility 37, formsecond input data for the second processing function 70, which as secondoutput data 69, should deliver a refined mask 71. In order to train thesecond processing function 70, a generative adversarial network (GAN) iscreated, in that a discriminator network 72 that serves to discriminatebetween x-ray images 74 corrected by the refined mask 71 present byaddition 73 and true x-ray images 65 is inserted. As output, thediscriminator network 72 delivers an adversarial loss 75 orclassification loss, which, in simple terms, expresses how unrealisticthe corrected x-ray image 74 still is by comparison with the realisticx-ray image 65. This adversarial loss is to be minimized and is thusused during the training process 76 for fitting the generator network 69as well as the discriminator network 72, as is basically known.

The result of the training process 76 is then the trained secondprocessing function 70, which delivers refined masks 71.

The boundary conditions serve various purposes and may, for example,make sure that refined masks 71 do not deviate too much from thespecification or the space of possible masks 64, that the smoothness ofthe mask 71 is provided and that only a specific set of arithmeticaloperations may be carried out in order to get from the input mask 64 tothe refined mask 71. In this way, the generator network 69 (e.g., basedon physical observations, such as an analysis of the space of masks 53obtained in the acts S1 and S2) is prevented from creating unrealisticoutputs, which may then irritate medical personnel if applied (e.g., bycausing artifacts to arise).

The addition of the trained second processing function 70 for correctionallows aspects, such as, where necessary, variable geometry and positionof the ROI 45 due to the jittering of the focus point 41 at low pulselengths, movements of the ROI 45 (e.g., due to eye tracking), effectscaused by patients recorded, such as beam hardening and the like, to betaken into consideration. At the same time, the mask 64 may be adaptedwith respect to the general brightness stabilization.

FIG. 10 shows a flow diagram of an exemplary embodiment of a correctionmethod. According to the method, in act S6, using first and secondphysical parameters 54, 55 (e.g., first input data 67), an x-ray image48 to be corrected is recorded and logarithmically transformed in actS7. The trained first processing function 63 is then used in order todetermine the mask 64 to be refined, in that first, in act S8, theassignment rule 62 is used in order to determine the latent spacerepresentation 60; after this, in act S9, the trained decoder 59 is usedin order to determine the mask 64 to be refined from the latent spacerepresentation 60 determined. The x-ray image 48 to be corrected, themask 64 to be refined, and also the boundary condition 68 form thesecond input data for the generator network 69 provided as secondtrained processing function, which thus delivers the refined mask 71 asoutput data. In act S10, the refined mask 71, in order to achieve thebrightness compensation, is corrected to the x-ray image 48 to becorrected, so that a corrected x-ray image 77 arises.

A denoising may then be applied to this corrected x-ray image 77 inorder to further improve the image quality and in this way possibly toallow a further reduction of the x-ray dose.

FIG. 11 explains the functional layout of one embodiment of a provisionsystem 40 that may also be referred to as a training system. Theprovision system 50 has a first training interface 78, via which thefirst training data (e.g., masks 53 with assigned physical parameters54, 55) are accepted. In a first training unit 79, the autoencoder 56 istrained as described in accordance with FIG. 8 (cf., arrow 57). Afterthis, in a rule determination unit 80, the assignment rule in accordancewith act S3 may be determined.

A second training interface 81 in the present case involves an internalinterface, via which the trained first processing function 63 may beprovided as a combination of the assignment rule 62 and the traineddecoder 59.

The provision system 40 now also has a third training interface 82, viawhich the second training data for the second stage is accepted (e.g.,the x-ray images 65 and 66 as well as the first input data 67 (physicalparameters 54, 55) in accordance with FIG. 9 ). A second training unit83 trains the generator network 69 as the second processing function 70using this second training data, as has been described in detail withregard to FIG. 9 , in the sense of an adversarial training. The firstprocessing function 62 has then been trained by representation learning.The correction algorithm, including the trained first processingfunction 62 and the trained second processing function 70 is thenprovided via a fourth training interface 84.

The correction system 39 in accordance with FIG. 12 includes a firstconnection interface 85, via which the correction algorithm may beaccepted from the fourth training interface 84 of the provision system40. X-ray images to be corrected 48 may be accepted via a secondapplication interface 86, to which the corresponding system settings(e.g., the first and second physical parameters 54, 55) are thenassigned accordingly. This makes it possible, in a mask determinationunit 87, to determine the refined correction mask 71, in that thetrained first and second processing functions 63, 70 are appliedaccordingly. In a correction unit 88, the mask 71 may then be used inorder to correct the x-ray image 48 to be corrected. In the correctionunit 88, a denoising of the corrected x-ray image 77 may also beundertaken in addition, before the image is provided to the thirdapplication interface 89.

The correction algorithm also contains the actual correction act (e.g.,the addition of mask 71 and x-ray image 48 to be corrected).

Although the invention has been illustrated and described in greaterdetail by the exemplary embodiments, the invention is not restricted bythe disclosed examples, and other variations may be derived herefrom bythe person skilled in the art without departing from the scope ofprotection of the invention.

The elements and features recited in the appended claims may be combinedin different ways to produce new claims that likewise fall within thescope of the present invention. Thus, whereas the dependent claimsappended below depend from only a single independent or dependent claim,it is to be understood that these dependent claims may, alternatively,be made to depend in the alternative from any preceding or followingclaim, whether independent or dependent. Such new combinations are to beunderstood as forming a part of the present specification.

While the present invention has been described above by reference tovarious embodiments, it should be understood that many changes andmodifications can be made to the described embodiments. It is thereforeintended that the foregoing description be regarded as illustrativerather than limiting, and that it be understood that all equivalentsand/or combinations of embodiments are intended to be included in thisdescription.

The invention claimed is:
 1. A method that is computer-implemented andis for provision of a correction algorithm for an x-ray image that hasbeen recorded with an x-ray source emitting an x-ray radiation field, afilter device spatially modulating an x-ray radiation dose, and an x-raydetector, wherein the correction algorithm comprises a trained firstprocessing function configured, from first input data that comprises atleast one first physical parameter describing the x-ray radiation field,a measurement, or the x-ray radiation field and the measurement, and atleast one second physical parameter describing the spatial modulation ofthe filter device, to determine first output data that comprises a maskfor brightness compensation with regard to the spatial modulation of thefilter device in the x-ray image, the method comprising: providing firsttraining data comprising first training datasets each with a mask,wherein each of the first training datasets is assigned the at least onefirst physical parameter and the at least one second physical parameterof the first input data assigned for determining the mask, providing anautoencoder for masks, wherein the autoencoder has an encoder fordetermining a latent space representation of the mask and a decoder fordetermining a comparison mask from the latent space representation;training the autoencoder using the first training data; determining anassignment rule between the at least one first physical parameter andthe at least one second physical parameter of the first input data,which are assigned to the first training datasets, and the latent spacerepresentations of the masks of the respective first training dataset;and providing the trained first processing function as a combination ofthe assignment rule and the trained decoder.
 2. The method of claim 1,wherein the filter is a region of interest (ROI) filter.
 3. The methodof claim 1, wherein: a larger number of latent space parameters of thelatent space representation than physical parameters of the first inputdata are used; 3 to 30 physical parameters of the first input data, 3 to30 latent space parameters of the latent space representation, or acombination thereof are used; the method further comprises determiningat least one functional relationship at least partly by fitting,interpolation, extrapolation, or any combination thereof; or anycombination thereof.
 4. The method of claim 1, wherein: the at least onefirst physical parameter comprises: a tube voltage of the x-ray source;a tube current of the x-ray source; a pre-filter parameter, an apertureparameter, or the pre-filter parameter and the aperture parameter; adistance of the x-ray source to the x-ray detector; a distance of thex-ray source to the filter device; a distance of the filter device fromthe x-ray detector; a pulse length of an x-ray pulse creating the x-rayradiation field; a number of x-ray pulses since a beginning of arecording of a series of x-ray images; at least one focal parameterdescribing a geometry of a focus point; a zoom of the x-ray detector; anorientation of the x-ray detector; a frame rate of the x-ray detector;or any combination thereof, and the at least one second physicalparameter comprises: a material of the filter device; at least onefilter thickness parameter describing a filter thickness course; atleast one change over time of time parameters describing the spatialmodulation; or any combination.
 5. The method of claim 1, wherein thefirst training datasets are determined from x-ray images recorded withand without the filter device.
 6. The method of claim 1, wherein thecorrection algorithm further comprises a second processing functiondownstream of the first trained processing function for refining themask determined by the first processing function, wherein the secondprocessing function has a generator network that uses as second inputdata an x-ray image, for which a refined mask is to be determined, and amask to be refined determined using the trained first processingfunction for the physical parameters of the first input data of thex-ray image of the second input data, wherein for training of the secondprocessing function, the method further comprises: providing secondtraining data comprising x-ray images of an object recorded with andwithout the filter device with assigned physical parameters of the firstinput data; providing a discriminator network for discriminating betweenreal x-ray images recorded without the filter device and x-ray imagescorrected using the refined mask obtained as second output data forcompletion of a generative adversarial network; for training thegenerator network and the discriminator network, using an output of thediscriminator network, by comparison of a corrected x-ray image of asecond training dataset with the x-ray image, recorded without thefilter device, of the second training dataset for fitting the generatornetwork and the discriminator network; and providing the correctionalgorithm comprising a combination of the trained first processingfunction and the trained second processing function.
 7. The method asclaimed in claim 6, further comprising receiving, by the generatornetwork, at least during training of the generator network, as furthersecond input data, at least one boundary condition restricting secondoutput data of the generator network.
 8. The method of claim 7, whereinthe at least one boundary condition comprises: a boundary conditionrestricting a deviation of the refined mask from the mask to be refined,a space of a possible mask to be refined, or a combination thereof; aboundary condition ensuring a smoothness of the refined mask; a boundarycondition restricting a type of arithmetical operations for obtainingthe refined mask from the mask to be refined; or any combinationthereof.
 9. The method of claim 6, wherein the x-ray images arelogarithmically transformed before being used as the second input data,the second training data, or the second input data and the secondtraining data.
 10. A method that is computer-implemented and is forcorrection of an x-ray image that was recorded with an x-ray sourceemitting an x-ray radiation field, a filter device spatially modulatingan x-ray radiation dose, and an x-ray detector, using a correctionalgorithm, the method comprising: providing the correction algorithm,wherein the correction algorithm comprises a trained first processingfunction configured, from first input data that comprises at least onefirst physical parameter describing the x-ray radiation field, ameasurement, or the x-ray radiation field and the measurement, and atleast one second physical parameter describing the spatial modulation ofthe filter device, to determine first output data that comprises a maskfor brightness compensation with regard to the spatial modulation of thefilter device in the x-ray image, the providing of the correctionalgorithm comprising: providing first training data comprising firsttraining datasets each with a mask, wherein each of the first trainingdatasets is assigned the at least one first physical parameter and theat least one second physical parameter of the first input data assignedfor determining the mask, providing an autoencoder for masks, whereinthe autoencoder has an encoder for determining a latent spacerepresentation of the mask and a decoder for determining a comparisonmask from the latent space representation; training the autoencoderusing the first training data; determining an assignment rule betweenthe at least one first physical parameter and the at least one secondphysical parameter of the first input data, which are assigned to thefirst training datasets, and the latent space representations of themasks of the respective first training dataset; and providing thetrained first processing function as a combination of the assignmentrule and the trained decoder, wherein the correction algorithm furthercomprises a second processing function downstream of the first trainedprocessing function for refining the mask determined by the firstprocessing function, wherein the second processing function has agenerator network that uses as second input data an x-ray image, forwhich a refined mask is to be determined, and a mask to be refineddetermined using the trained first processing function for the physicalparameters of the first input data of the x-ray image of the secondinput data; for training of the second processing function: providingsecond training data comprising x-ray images of an object recorded withand without the filter device with assigned physical parameters of thefirst input data; providing a discriminator network for discriminatingbetween real x-ray images recorded without the filter device and x-rayimages corrected using the refined mask obtained as second output datafor completion of a generative adversarial network; and for training thegenerator network and the discriminator network, using an output of thediscriminator network, by comparison of a corrected x-ray image of asecond training dataset with the x-ray image, recorded without thefilter device, of the second training dataset for fitting the generatornetwork and the discriminator network; and providing the correctionalgorithm comprising a combination of the trained first processingfunction and the trained second processing function; determining therefined mask using the correction algorithm from at least the firstinput data assigned to the x-ray image to be corrected; and using therefined mask for correction of the x-ray image to be corrected.
 11. Themethod of claim 10, further comprising applying a denoising method tothe corrected x-ray image.
 12. A system for provision of a correctionalgorithm for an x-ray image that was recorded with an x-ray sourceconfigured to emit an x-ray radiation field, a filter device configuredto spatially modulate an x-ray radiation dose, and an x-ray detector,wherein the correction algorithm comprises a trained first processingfunction that, from first input data that comprises at least one firstphysical parameter describing the x-ray radiation field, a measurement,or the x-ray radiation field and the measurement, and at least onesecond physical parameter describing the spatial modulation of thefilter device, is configured to determine first output data thatcomprises a mask for brightness compensation with regard to the spatialmodulation of the filter device in the x-ray image, the systemcomprising: a first training interface configured to provide firsttraining data, the first training data comprising first trainingdatasets each with a mask, wherein the at least one first physicalparameter and the at least one second physical parameter of the firstinput data assigned for determining the mask are assigned to each firsttraining dataset; a first training unit configured to train anautoencoder for masks, wherein the autoencoder has an encoder configuredto determine a latent space representation of the mask and a decoderconfigured to determine a comparison mask from the latent spacerepresentation, using the first training data; a rule determination unitconfigured to determine an assignment rule between the at least onefirst physical parameter and the at least one second physical parameterof the first input data, which is assigned to the first trainingdatasets, and the latent space representations of the masks of therespective first training dataset; and a second training interfaceconfigured to provide the trained first processing function as acombination of the assignment rule and the trained decoder.
 13. A systemfor correction of an x-ray image that was recorded with an x-ray sourceconfigured to emit an x-ray radiation field, a filter device configuredto spatially modulate an x-ray radiation dose, and an x-ray detector,the system comprising: a first application interface configured toaccept a correction algorithm provided from a system for provision of acorrection algorithm for an x-ray image that was recorded with an x-raysource configured to emit an x-ray radiation field, a filter deviceconfigured to spatially modulate an x-ray radiation dose, and an x-raydetector, wherein the correction algorithm comprises a trained firstprocessing function that, from first input data that comprises at leastone first physical parameter describing the x-ray radiation field, ameasurement, or the x-ray radiation field and the measurement, and atleast one second physical parameter describing the spatial modulation ofthe filter device, is configured to determine first output data thatcomprises a mask for brightness compensation with regard to the spatialmodulation of the filter device in the x-ray image, the system forprovision of the correction algorithm comprising: a first traininginterface configured to provide first training data, the first trainingdata comprising first training datasets each with a mask, wherein the atleast one first physical parameter and the at least one second physicalparameter of the first input data assigned for determining the mask areassigned to each first training dataset; a first training unitconfigured to train an autoencoder for masks, wherein the autoencoderhas an encoder configured to determine a latent space representation ofthe mask and a decoder configured to determine a comparison mask fromthe latent space representation, using the first training data; a ruledetermination unit configured to determine an assignment rule betweenthe at least one first physical parameter and the at least one secondphysical parameter of the first input data, which is assigned to thefirst training datasets, and the latent space representations of themasks of the respective first training dataset; and a second traininginterface configured to provide the trained first processing function asa combination of the assignment rule and the trained decoder; a secondapplication interface configured to accept the x-ray image to becorrected along with assigned first and second physical parameters; amask determination unit configured to determine a refined mask using thecorrection algorithm from at least the first input data assigned to thex-ray image to be corrected, wherein the correction algorithm furthercomprises a second processing function downstream of the first trainedprocessing function and configured to refine the mask determined by thefirst processing function, wherein the second processing function has agenerator network configured to use as second input data an x-ray image,for which the refined mask is to be determined, and a mask to be refineddetermined using the trained first processing function for the physicalparameters of the first input data of the x-ray image of the secondinput data, the determination of the refined mask comprising: fortraining of the second processing function: provision of second trainingdata comprising x-ray images of an object recorded with and without thefilter device with assigned physical parameters of the first input data;provision of a discriminator network configured to discriminate betweenreal x-ray images recorded without the filter device and x-ray imagescorrected using the refined mask obtained as second output data forcompletion of a generative adversarial network; and for training thegenerator network and the discriminator network, use of an output of thediscriminator network, by comparison of a corrected x-ray image of asecond training dataset with the x-ray image, recorded without thefilter device, of the second training dataset for fitting the generatornetwork and the discriminator network; and provision of the correctionalgorithm comprising a combination of the trained first processingfunction and the trained second processing function; a correction unitconfigured to use the refined mask for correction of the x-ray image tobe corrected; and a third application interface configured to provide acorrected x-ray image.
 14. An x-ray facility comprising: an x-raysource; an x-ray detector; a filter facility configured to spatiallymodulate an x-ray radiation dose; and a controller comprising: a systemfor correction of an x-ray image that was recorded with an x-ray sourceconfigured to emit an x-ray radiation field, a filter device configuredto spatially modulate an x-ray radiation dose, and an x-ray detector,the system comprising: a first application interface configured toaccept a correction algorithm provided from a system for provision of acorrection algorithm for an x-ray image that was recorded with an x-raysource configured to emit an x-ray radiation field, a filter deviceconfigured to spatially modulate an x-ray radiation dose, and an x-raydetector, wherein the correction algorithm comprises a trained firstprocessing function that, from first input data that comprises at leastone first physical parameter describing the x-ray radiation field, ameasurement, or the x-ray radiation field and the measurement, and atleast one second physical parameter describing the spatial modulation ofthe filter device, is configured to determine first output data thatcomprises a mask for brightness compensation with regard to the spatialmodulation of the filter device in the x-ray image, the system forprovision of the correction algorithm comprising: a first traininginterface configured to provide first training data, the first trainingdata comprising first training datasets each with a mask, wherein the atleast one first physical parameter and the at least one second physicalparameter of the first input data assigned for determining the mask areassigned to each first training dataset; a first training unitconfigured to train an autoencoder for masks, wherein the autoencoderhas an encoder configured to determine a latent space representation ofthe mask and a decoder configured to determine a comparison mask fromthe latent space representation, using the first training data; a ruledetermination unit configured to determine an assignment rule betweenthe at least one first physical parameter and the at least one secondphysical parameter of the first input data, which is assigned to thefirst training datasets, and the latent space representations of themasks of the respective first training dataset; and a second traininginterface configured to provide the trained first processing function asa combination of the assignment rule and the trained decoder; a secondapplication interface configured to accept the x-ray image to becorrected along with assigned first and second physical parameters; amask determination unit configured to determine a refined mask using thecorrection algorithm from at least the first input data assigned to thex-ray image to be corrected, wherein the correction algorithm furthercomprises a second processing function downstream of the first trainedprocessing function and configured to refine the mask determined by thefirst processing function, wherein the second processing function has agenerator network configured to use as second input data an x-ray image,for which the refined mask is to be determined, and a mask to be refineddetermined using the trained first processing function for the physicalparameters of the first input data of the x-ray image of the secondinput data, the determination of the refined mask comprising: fortraining of the second processing function:  provision of secondtraining data comprising x-ray images of an object recorded with andwithout the filter device with assigned physical parameters of the firstinput data;  provision of a discriminator network configured todiscriminate between real x-ray images recorded without the filterdevice and x-ray images corrected using the refined mask obtained assecond output data for completion of a generative adversarial network;and  for training the generator network and the discriminator network,use of an output of the discriminator network, by comparison of acorrected x-ray image of a second training dataset with the x-ray image,recorded without the filter device, of the second training dataset forfitting the generator network and the discriminator network; andprovision of the correction algorithm comprising a combination of thetrained first processing function and the trained second processingfunction; a correction unit configured to use the refined mask forcorrection of the x-ray image to be corrected; and a third applicationinterface configured to provide a corrected x-ray image.
 15. In anon-transitory computer-readable storage medium that stores instructionsexecutable by a computer for provision of a correction algorithm for anx-ray image that has been recorded with an x-ray source emitting anx-ray radiation field, a filter device spatially modulating an x-rayradiation dose, and an x-ray detector, wherein the correction algorithmcomprises a trained first processing function configured, from firstinput data that comprises at least one first physical parameterdescribing the x-ray radiation field, a measurement, or the x-rayradiation field and the measurement, and at least one second physicalparameter describing the spatial modulation of the filter device, todetermine first output data that comprises a mask for brightnesscompensation with regard to the spatial modulation of the filter devicein the x-ray image, the instructions comprising: providing firsttraining data comprising first training datasets each with a mask,wherein each of the first training datasets is assigned the at least onefirst physical parameter and the at least one second physical parameterof the first input data assigned for determining the mask, providing anautoencoder for masks, wherein the autoencoder has an encoder fordetermining a latent space representation of the mask and a decoder fordetermining a comparison mask from the latent space representation;training the autoencoder using the first training data; determining anassignment rule between the at least one first physical parameter andthe at least one second physical parameter of the first input data,which are assigned to the first training datasets, and the latent spacerepresentations of the masks of the respective first training dataset;and providing the trained first processing function as a combination ofthe assignment rule and the trained decoder.